The present invention relates generally to security and surveillance and relates more specifically to methods for classifying and tracking surveillance targets.
Many venues such as retail environments, factories, offices, transportation hubs, and the like employ surveillance systems in order to ensure customer safety, national security, and/or operational efficiency. Many conventional surveillance systems distribute large sets of surveillance devices (e.g., cameras, sensors, and the like) within a monitored area in order to detect illicit, unsafe, and/or unauthorized events. These devices generate surveillance data in the forms of streams and/or data points that are typically forwarded to a central location for review (e.g., by a human operator).
The computational cost of processing all of the surveillance data, however, can be prohibitive. For instance, if the surveillance system deploys a plurality of devices, all of which are continuously generating surveillance data, a potentially enormous amount of surveillance data will be generated. It may be difficult, if not impossible, for a human operator to efficiently review all of this data and therefore to respond in a timely manner to an event.
Moreover, events that may be normal for one type of person may be abnormal for another type of person. For instance, in a bank environment, it might be “normal” (i.e., likely not worthy of a security alert) for a teller or cashier to venture behind the counter, but “abnormal” (i.e., potentially worthy of a security alert) for a customer to do the same. Many conventional surveillance systems, however, have difficulty distinguishing between normal and abnormal events based on the type (or role) of the person involved in the events.